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开发神经网络以检测代谢功能障碍相关的脂肪性肝病中的肝硬化症

Masashi Hirooka¹, Teruki Miyake², Ryo Yano²

¹Total Medical Support Center, Ehime University Hospital, Toon, Japan.

Gastro hep advances

|October 27, 2025

概括

一种新的神经网络模型使用常见的健康数据准确检测代谢功能障碍相关的脂肪性肝病 (MASLD). 这种人工智能工具超越了早期MASLD查的传统方法,改善了患者风险分层.

科学领域:

肝病学肝病学是一种肝病学.
人工智能的人工智能
医学诊断医学诊断医学诊断

背景情况:

与代谢功能障碍相关的脂肪性肝病 (MASLD) 需要早期识别才能有效管理.
目前用于MASLD检测的非侵入性指数的准确性有限,并依赖于线性假设.

研究的目的:

开发和验证一种新的神经网络模型,用于非侵入性检测肝硬化症.
将神经网络模型的性能与传统的MASLD指数进行比较.

主要方法:

一个feedforward神经网络在来自17,465名日本健康检查参与者的临床和生化数据上进行了训练.
该模型对9,039名参与者进行了内部验证,并对来自NHANES队列的9,759名参与者进行了外部验证.
使用AUC,校准和决策曲线分析来评估性能,通过超声波定义的肝脏肥胖症.

主要成果:

神经网络实现了高预测准确度,AUC为0.922 (内部) 和0.924 (外部验证).
该模型的表现明显优于现有的指数,如脂肪肝指数和肝脂肪症指数 (P < .001).
鉴定到的关键预测因素是腹周和体重指数,在最佳切线时具有89%的灵敏度和86%的特异性.

结论:

与传统方法相比,开发的神经网络模型在检测中度至重度肝硬化症方面表现优异.
这种人工智能驱动的方法为初级保健和资源有限的环境中早期MASLD查提供了可扩展的解决方案.

关键词:

人工智能的人工智能是人工智能.肝脏肥胖症肝脏肥胖症代谢功能障碍代谢功能障碍神经网络模型的神经网络模型进行非侵入性查.

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